BIC, a world leader in stationery, lighters, and shavers, is partnering with technology company Iprova to launch the BIC-Iprova Invention Lab to enhance BIC’s Innovation and Research & Development (R&D) capability. Using Iprova’s machine learning-based, data-driven approach to invention, the Lab will help BIC anticipate and answer rapidly changing consumer needs with unexpected inventions that will benefit consumers. BIC is the first company to build an in-house data-dri... Read More

Often a regression model over fits the data on which it trains. Having a basic understanding of this concept is quite important before jumping into the techniques to remove its negative impact. So, let's become familiar with the practice of regularization Read More

Support Vector Machines or SVMs may sound intimidating, but they really aren’t. When I first heard about them, I became a little curious and wanted to know more. Read More

Machine Learning Algorithm used most commonly and the Types of ML. Just a short-notes on these. Read More

Before getting into the definition, let’s first understand why we need Machine Learning and how it came into existence. Read More

This article is on where to start with Machine Learning. Read More

I am sending you my first article about the 'Game Changer' series. I am writing about my experience as an intern at Boemska, exploring the world of SAS and big enterprise analytics. In this article, I wanted to focus mainly on the exploration of the characteristics and advantages of the SAS® Visual Data Mining and Machine Learning solution of SAS Viya. Read More

Bias and variance are two major issues we try to manage in machine learning. We are always looking for a trade-off between the two. Bias has to do with the training data while variance has to do with the model output. Read More

Developers who are new to programming can start with Python by joining Python training classes and then move to other languages. Python’s object-oriented principles are compatible with other languages like JavaScript, Perl, Ruby, and C#. Read More

This paper identifies a gap in the application of machine learning in the industry, especially petroleum. Recent development has led to massive employment of data scientists who know little about the problem domain. It is recommended that data scientists work closely with domain experts to build robust solutions rather than working in isolation. Read More